估計公司資產價值及股票隱含報酬率

Abstract

傳統預測股票市場趨勢的方法乃基於使用歷史資料來預測未來，但當市場有重大事件發生，使得趨勢出現重大轉折時，這些方法較無法捕捉到市場的未來趨勢。在本文中，我們使用衍生性金融商品的評價方法，配合上衍生性商品、股票，及債券的每日交易資料，計算股價的隱含報酬率。由於交易資料蘊含市場對於未來的期望，故較能夠對於市場突然的變化做出反應。本文使用了Merton Asset Pricing Model(1974)，及Black & Cox(1976)提出的First-Passage Model，分別做為實驗的模型設定，而實驗數值結果證實了本文方法的優越性。

Traditional methods for predicting stock trends basically use historical trading data to predict the future trends. When vital events occur and the market reaches the turning point, these methods usually fail to capture the future trend of market. In this thesis, we use the derivative pricing formulae and daily trading data from both the stock and bond markets to calculate the implied stock return. Since the trading data reflect the investors’ expectation for the future market’s trend, our method is more capable to capture the change of future trend of the market. The settings of the research follow Merton’s asset pricing model (1974) and Black & Cox’s first passage model (1976). Numerical results are given to verify the superiority of our model.